Multi-band infrared camera system optimized for skin detection

ABSTRACT

What is disclosed is a system and method for selecting the optimal wavelength ban combination for a multi-band infrared camera system which is optimized for skin detection. An objective function is constructed specifically for this application from classified performance and the algorithm generates wavelengths by maximizing the objective function. A specific wavelength band combination is selected which maximizes the objective function. Also disclosed is a 3-band and 4-band camera system with filters each having a transmittance of one of a combination of wavelength bands optimized to detect skin in the infrared band. The camera systems disclosed herein find their intended uses in a wide array of vehicle occupancy detection systems and applications. Various embodiments are disclosed.

TECHNICAL FIELD

The present invention is directed to a method for determining an optimalwavelength band combination for filters of an infrared camera used foracquiring infrared images containing skin tissue, and an infrared camerawith filters selected using the optimization method.

BACKGROUND

Skin detection using a multi-band near-infrared (NIR) camera and itsapplication to face recognition and human occupancy detection in avehicle is well documented. As shown in FIG. 5, the characteristic ofhuman reflectance in the NIR range includes a drop from 1000 nm to 1450nm, a small rise to 1650 nm, a drop to 1900 nm, and then remainsrelatively constant for a range of wavelengths. As such, the fullextraction of skin reflectances in the image requires many wavelengthbands. Simply adding an increased number of wavelength bands to thecamera increases the cost of the imaging system.

Accordingly, what is needed is an infrared camera system designed tomaximize the detection of skin in infrared images acquired over 3 or 4filter bands thereby enabling a cost-effective solution to vehicleoccupancy detection systems and applications.

INCORPORATED REFERENCES

The following U.S. patents, U.S. patent applications, and Publicationsare incorporated herein in their entirety by reference.

“A Multi-Filter Array For A Multi-Resolution Multi-Spectral Camera”,U.S. patent application Ser. No. 13/239,642, by Mestha et al., whichdiscloses a multi-filter array for a multi-resolution and multi-spectralcamera system for simultaneous spectral decomposition with a spatiallyand spectrally optimized multi-filter array suitable for image objectidentification.

“Determining A Number Of Objects In An IR Image”, U.S. patentapplication Ser. No. 13/086,006, by Wang et al., which discloses acorrelation method and a best fitting reflectance method for classifyingpixels in an IR image.

“Determining A Total Number Of People In An IR Image Obtained Via An IRImaging System”, U.S. patent application Ser. No. 12/967,775, by Wang etal., which discloses a ratio method for classifying pixels in an IRimage.

“Determining A Pixel Classification Threshold For Vehicle OccupancyDetection”, U.S. patent application Ser. No. 13/324,308, by Wang et al.,which discloses a method for determining a threshold used for pixelclassification.

“Method For Classifying A Pixel Of A Hyperspectral Image In A RemoteSensing Application”, U.S. patent application Ser. No. 13/023,310, byMestha et al., which discloses a system and method for simultaneousspectral decomposition suitable for image object identification andcategorization for scenes and objects under analysis.

“Reconfigurable MEMS Fabry-Perot Tunable Matrix Filter Systems AndMethods”, U.S. Pat. No. 7,355,714, to Wang et al.

“Two-Dimensional Spectral Cameras And Methods For Capturing SpectralInformation Using Two-Dimensional Spectral Cameras”, U.S. Pat. No.7,385,704, to Mestha et al.

“Fabry-Perot Tunable Filter Systems And Methods”, U.S. Pat. No.7,417,746, to Lin et al.

BRIEF SUMMARY

What is disclosed is a system and method for selecting the optimalwavelength band combination for a multi-band infrared camera which isoptimized for skin detection. An objective function is disclosed forthis application. A specific wavelength band combination is selectedwhich maximizes the objective function. Also disclosed is a 3-band and4-band IR camera with filters each having a transmittance in one of thewavelength bands selected according to the optimization method disclosedherein. The IR camera systems disclosed herein find their uses in a widearray of vehicle occupancy detection systems, skin detection and facialrecognition applications.

In one embodiment, a method is disclosed for determining a combinationof bands for filters for an infrared camera used for skin detection. Themethod involves receiving at least one infrared image containing, atleast in part, an area of exposed skin along with other objects. Thepixels in the image are labeled as belonging to skin versus non-skinobjects and split into a training set and a test set. A filter bandcombination is selected for evaluation. A pixel classification algorithm(a “classifier”) is derived from the training data for the given set offilter bands. This classifier is used to classify each pixel in the testset as belonging to skin or non-skin objects. An objective function iscalculated that measures the performance of this classifier on the testdata for this filter band combination. The process is repeated fordifferent filter band combinations and the results stored. A filter bandcombination is selected which maximized the objective function.

What is also disclosed is an infrared camera optimized for skindetection. In one embodiment, the IR camera comprises optics forfocusing a percentage of light reflected from an object onto an array ofdetectors such that the reflected light can be spatially resolved toform an infrared image, the detectors sampling radiation emitted by atleast one light source and recording intensity values for multiplepixels locations along a two dimensional grid. In various embodiments,the array can comprise Mercury Cadmium Telluride (HgCdTe) detectors,Indium Arsenide (InAs), Indium Gallium Arsenide (InGaAs) detectors,Indium Antimonide (InSb) detectors, or Lead Sulphide (PbS) detectors. Afirst filter has a transmittance peaked in the range of 1000 nm to 1150nm. A second filter has a transmittance peaked in the range of 1400 nmto 1500 nm. A third filter has a transmittance peaked in the range of1550 nm to 1650 nm. The filters can be a geometrically patterned array,or can comprise any of: a thin film filter for simultaneous multi-imagecapture of different spectral bands with a mosaic pattern betweenfilters, a Fabry-Perot filter for simultaneous multi-image capture ofdifferent spectral bands, or a filter wheel for non-simultaneousmulti-image capture of different spectral bands. The camera further hasa plurality of outputs for outputting at least one reflectance value perchannel, a processor for processing reflectances associated with pixelsin the image, a storage device, and a controller for enabling theselection of the detectors.

What is also disclosed is an infrared camera with a first, second, thirdand fourth filter. This IR camera is similarly configured to the3-filter IR camera but further comprises a fourth filter with atransmittance peaked in the range of 1150 nm to 1400 nm.

Many features and advantages of the above-described method will becomereadily apparent from the following detailed description andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the subject matterdisclosed herein will be made apparent from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 illustrates one embodiment of an IR camera with four filters;

FIG. 2 illustrates one embodiment of the present method for determininga combination of bands for filters of an infrared (IR) camera used foracquiring IR images of humans in a motor vehicle, in accordance with theteachings hereof;

FIG. 3 is a continuation of the flow diagram of FIG. 2 with flowprocessing continuing with respect to node A;

FIG. 4 illustrates a block diagram of one example networked computingsystem for implementing various aspects of the present method as shownand described with respect to the flow diagrams of FIGS. 2 and 3; and

FIG. 5 is a graph which plots human reflectance in the NIR range.

DETAILED DESCRIPTION

What is disclosed is a system and method for selecting a wavelength bandcombination for a multi-band infrared camera system which is optimizedfor skin detection.

NON-LIMITING DEFINITIONS

An “infrared image” is an IR image captured of a subject of interestusing the multi-band IR camera system disclosed herein. Afully-populated infrared image consists of a plurality of pixels witheach pixel having an associated IR band vector comprising a total of Nintensity values measured in terms of captured reflectances centeredabout each of N wavelength bands, where N=3 or N=4. Pixels in thecaptured images contain spectral information. Images captured by ahyper-spectral camera have contiguous spectral planes, whereasmulti-spectral images have non-contiguous spectral planes.Hyper-spectral images are processed into a hyper-spectral image datacube comprising a 3D matrix constructed of a combination of 2D imagedata and 1D spectral data. The infrared image may comprise a timevarying video signal.

A “multi-band IR camera system” can be either a multi-spectral or ahyper-spectral apparatus. Both embodiments generally comprise an arrayof detectors which capture IR light reflected from a target and outputsan infrared image of the target. As disclosed herein, the IR camerasystem has at least one light source for illuminating the target objectand a detector array with each detector having a respective narrowband-pass filter. The spatial resolution of the camera refers to thenumber of pixels in the image captured by the camera. A higher spatialresolution means a higher density of pixels. The spectral resolution ofthe camera refers to the camera's ability to resolve features in theelectromagnetic spectrum and is usually denoted by Δλ, (often referredto as the resolving power of the device), defined as:

${R = \frac{\lambda}{\Delta\lambda}},$

where Δλ is the smallest difference in wavelengths that can bedistinguished at wavelength λ. In different embodiments, the IR camerasdisclosed herein include a plurality of outputs for outputtingreflectance values on a per-channel basis, and may further comprise aprocessor and a storage device for processing and storing reflectancevalues.

A “filter”, as used herein, effectuates the transmittance of a desiredwavelength band while rejecting wavelengths outside the band such thatthe light received by the optics of the imaging device is restricted tothe bands of interest. The filters can be a thin film filter forsimultaneous multi-image capture of different spectral bands with amosaic pattern between filters. A thin film filter is a coating which isapplied to the optics during the optical fabrication process. Thin filmcoatings can be, for example, Calcium Fluoride (CaF2), Barium Fluoride(BaF2), Zinc Selenide (ZnSe), Zinc Sulfide (ZnS), to name a few. Thinfilm filters are readily available in various streams of commerce. Forexample, the Reynard Corporation has experience working with a widearray of thin film coatings for infrared applications including infraredmaterials supplied by their customers. The filters can be a Fabry-Perotfilter for simultaneous multi-image capture of different spectral bands.Embodiments of Fabry-Perot filters are disclosed in theabove-incorporated references: U.S. Pat. No. 7,355,714 entitled:“Reconfigurable MEMS Fabry-Perot Tunable Matrix Filter Systems AndMethods”, U.S. Pat. No. 7,385,704 entitled: “Two-Dimensional SpectralCameras And Methods For Capturing Spectral Information UsingTwo-Dimensional Spectral Cameras”, and U.S. Pat. No. 7,417,746 entitled:“Fabry-Perot Tunable Filter Systems And Methods”. The filters can be afilter wheel for non-simultaneous multi-image capture of differentspectral bands. A filter wheel is a wheel assembly of different infraredfilters which is rotatably mounted onto the infrared camera system toeffectuate non-simultaneous multi-image capture of different spectralbands. The filter wheel is usually manually rotated but, in differentconfigurations, can be made electro-mechanically rotatable. Filterwheels comprising customer provided filters are readily available invarious streams of commerce.

An “array of detectors” refers to sensors arranged in a two dimensionalgrid which sample radiation emitted by the illumination source andrecord intensity values at pixels locations along the grid. The detectorarray functions to spatially resolve the received reflectances to forman image and output the image to either a processor or a memory orstorage device. The sensitivities of the sensors in the detector arraycan be made selectable (i.e., tunable) using a controller. In variousembodiments, the detector comprises an array of Mercury CadmiumTelluride (HgCdTe) sensors, Indium Arsenide (InAs) sensors, IndiumGallium Arsenide (InGaAs) sensors, Indium Antimonide (InSb) sensors,and/or Lead Sulphide (PbS) sensors. It should be appreciated that thelist of sensors is not exhaustive and thus is not to be viewed aslimiting. Other sensors which are in existence or which may be developedare intended to fall with the scope of the appended claims.

“Optics” focus a percentage of the illuminator's reflected light ontothe detector array. Optics and detectors include components commonlyfound in commerce.

A “pixel classification algorithm” or “Classifier” is a method forclassifying a pixel in an infrared image such that objects in the imagecan be identified as belonging to a known material such as skin andnon-skin. Pixels in an infrared image can be classified using many knowntechniques, including a correlation method, a best fitting reflectancemethod, and a ratio method.

A “Correlation Method” refers to a method of pixel classificationwherein pixels of an IR image are classified based upon an amount ofcorrelation between a captured intensity of that pixel and a (scaled)intensity calculated from a model. The correlation method using amaterials spectra database containing pre-measured reflectances of knownmaterials. A theoretical pixel intensity for each object in the image iscalculated and the measured intensity of each pixel is compared to thetheoretical intensities to determine the amount of correlationtherebetween. In one embodiment, when taking a multiband image, theimage intensity is given by:

I _(c)(i)=α∫_(λ) ₁ ^(λ) ² I _(s)(λ)T _(G) ²(λ)F _(i)(λ)R_(o)(λ)D(λ)dλ  (1)

where i=1, 2 . . . N stands for the IR band using the i^(th) filterF_(i)(λ), I_(s)(λ) is the power spectrum of the illuminator, R_(o)(λ) isthe reflectance of the object inside the vehicle, either a human or abackground subject, T_(G)(λ) is the transmittance of a glass window,D(λ) is the quantum efficiency of the camera detector, λ₁ and λ₂specifies the wavelength range the camera integrates, and α is aconstant that depends on the angle and distance from the illuminationsource, the pixel's size, and the camera's integration time. Inprinciple, this constant can be calculated but, due to many factors thatcannot be determined accurately, it can be treated as a parameter. Forthe correlation method, this constant is cancelled.

The correlation method is used in conjunction with a database thatcontains pre-measured reflectance from human skins (hairs) or otherobjects, the transmittance of window glasses (side window orwindshield), the power spectra of the illuminators, the filtertransmittances, and the quantum efficiency curve of the detector. Thetheoretical camera intensity can then be calculated from Eq. (1) and theactual captured camera intensity for each pixel can be compared againstthe theoretical intensity. The coefficient is given by:

$\begin{matrix}{{C = \frac{\sum\limits_{i = 1}^{i = N}\; {\lbrack {I_{cm}(i)} \rbrack \lbrack {I_{cs}(i)} \rbrack}}{{\sqrt{\sum\limits_{i = 1}^{i = N}}\;\lbrack {I_{cm}(i)} \rbrack}^{2}{\sqrt{\sum\limits_{i = 1}^{i = N}\;}\lbrack {I_{cs}(i)} \rbrack}^{2}}},} & (2)\end{matrix}$

where I_(cm)(i) is the captured intensity of a pixel from the i^(th)wavelength band, I_(cs)(i) is the intensity of a pixel of the humantissue of the driver, and N is the total number of wavelength bands ofthe multi-band IR imaging system. If the intensity of the driver'sfacial pixel (with a particular reflectance) agrees with the measuredintensity of the pixel of the object, then the correlation will be high(close to 1). Otherwise, the correlation will be low (close to 0 ornegative). Pixels are classified based upon a comparison with athreshold.

A “Best Fitting Reflectance Method” is a pixel classification methodwhich cross-references measured pixel reflectances with reflectances ofknown materials in a materials spectral database and determines a bestfitting reflectance, i.e., a best match.

A “Ratio Method” is a pixel classification method classifies a pixel ashuman tissue vs. other materials if this ratio is larger or smaller thana threshold value.

An “objective function” is used to measure classifier performance foreach combination of wavelength bands. The optimum band combination ischosen that maximizes the objective function. In one embodiment, theobjective function is given by:

J=D _(skin) −D _(non-skin),

where, for a given pixel classification method, D_(skin) is a ratio ofthe number of correctly classified skin pixels to the total number ofskin pixels in the image, and D_(non-skin) is a ratio of the number ofnon-skin pixels that have been incorrectly classified as skin pixels tothe total number of non-skin pixels in the image.Example IR Camera with Four Filters

Reference is now being made to FIG. 1 which illustrates one embodimentof an IR camera 100 with four filters in accordance with the teachingshereof. Although the embodiment of FIG. 1 is shown having four filters,the illustration equally applies to an embodiment of an IR camera withthree filters in accordance with the teachings hereof.

In FIG. 1, reflected light 102 passes through an array of filters(collectively at 103) shown, in this embodiment, comprising four filters103A-D. Filter 103A has a transmittance in the range of 1000 nm to 1150nm; filter 103B has a transmittance in the range of 1400 nm to 1500 nm;filter 103C has a transmittance in the range of 1550 nm to 1650 nm; andfilter 103D has a transmittance in the range of 1150 nm to 1400 nm.Optics 104 has lens 105 that focus the received light 106 onto detectorarray 107 shown comprising an array of sensors 108 arranged in a twodimensional grid. It should be appreciated that array 108 isillustrative. Sensors 108 record intensity values at multiple pixelslocations. Detector array 107 sends the acquired spectral datacomprising the IR image 109 to processor 110 for processing and tomemory 111. Controller 112 is shown effectuating the control of any of:the filter array 103, the optics 104, and/or the detector array 107.Although controller 112 is shown integral to the IR camera, it may beexternal to the device. Processor 110 and memory 111 may be incommunication with a computing device (not shown) such as a desktopcomputer (410 of FIG. 4) or a special purposes computing system such asan ASIC. Any of the components of IR camera 100 may be placed incommunication with one or more remote devices over a network.

Flow Diagram of One Embodiment of the Present Method

Reference is now being made to the flow diagram of FIG. 2 whichillustrates one embodiment of the present method for determining acombination of bands for filters of an infrared (IR) camera used foracquiring IR images of skin. Flow processing begins at step 200 andimmediately proceeds to step 202.

At step 202, a plurality of infrared images are received. Each of theimages contains, at least in part, an area of exposed skin. The infraredimages can be retrieved directly from the IR imaging system used toacquire the images or retrieved from a memory or storage device forprocessing. The images may be acquired from a remote device over anetwork. The user may select a portion of the images for processing.

At step 204, label the pixels in the images as belonging to either skinor non-skin objects. The labeled pixels being referred to as “imagedata”.

At step 206, divide the image data into a training set and a test set.

At step 208, select a candidate set of wavelength bands (“filterbands”).

At step 210, derive a pixel classification algorithm (a “classifier”)that classifies pixel values in the training set corresponding to theselected candidate wavelength band combination as belonging to eitherskin or non-skin objects.

At step 212, use the classifier to classify pixels in the test set asbelonging to skin or non-skin.

At step 214, compute an objective function that measures the performanceof the pixel classifier on the test data set for this wavelength bandcombination. The values calculated for this particular wavelength bandcombination are saved to storage device 215.

Reference is now being made to the flow diagram of FIG. 3 which is acontinuation of the flow diagram of FIG. 2 with flow processingcontinuing with respect to node A.

At step 216, a determination is made whether more candidate wavelengthband combinations remain to be selected. If so, then processingcontinues with respect to node C wherein, at step 208, a next wavelengthband combination is selected. A pixel classification algorithm isderived for that classifies pixel values in the training setcorresponding to this next selected candidate set of filter bands asbelonging to either skin or non-skin objects. The pixel classificationalgorithm is used to classify pixels in this test set as belonging toskin or non-skin. An objective function is computed that measures theperformance of the pixel classifier on the test data set for thiscandidate wavelength band combination. The values of the objectivefunction are saved to storage device 215. The process repeats in such amanner until all desired candidate filter band combinations have beenprocessed.

At step 218, select a combination of filter bands that maximized theobjective function. Thereafter, in this particular embodiment, furtherprocessing stops.

It should be appreciated that the flow diagrams hereof are illustrative.One or more of the operative steps illustrated in the flow diagram maybe performed in a differing order. Other operations, for example, may beadded, modified, enhanced, condensed, integrated, or consolidated. Suchvariations are intended to fall within the scope of the appended claims.All or portions of the flow diagrams may be implemented partially orfully in hardware in conjunction with machine executable instructions.

Block Diagram of Example System

Reference is now being made to FIG. 4 which illustrates a block diagramof one example system capable of implementing various aspects of thepresent method shown and described with respect to the flow diagrams ofFIGS. 2 and 3.

The system 400 of FIG. 4 is shown comprising an image receiving module402 which receives infrared images 401 containing an area of exposedskin. Receiving module 402 buffers the images, labels the pixels in theimages as belonging to skin or non-skin objects, and divides the imagedata into a training set and a test set. Filter Band CombinationSelector 403 selects a wavelength band combination for processing.Candidate sets of filter bands are alternatively selected by a userusing, for example, the graphical user interface of workstation 410.Selector 403 receives the image data and passes the training and testdata sets to Pixel Classification Module 406 which derives a classifierthat classifies pixel values in the training set corresponding to theselected candidate filter band combination as belonging to either skinor non-skin. Module 406 further classifies pixels in the test set asbelonging to skin or non-skin. Objective Function Processor 404calculates the objective function value for each wavelength combinationbased upon the pixel-classified images. The object function measures theperformance of the pixel classifier for the selected filter bandcombination. The values of the objective function calculated for eachfilter band combination are stored to Memory 405. Processing repeats foreach filter band combination. Selection Module 407 identifies, fromMemory 405, the filter band combination that maximized the objectivefunction. The selected filter band combination is communicated toworkstation 410.

Workstation 410 is shown comprising a display 411 for enabling a displayof information for a user input and a keyboard 412 for making a userselection such as, for example, the user selecting the pixelclassification parameters or identifying areas in any of the receivedimages for processing. A user may use the graphical user interface toidentify or select one or more portions of the IR image such as, forinstance, a facial area where exposed skin is likely to be found in theimages. Various portions of the received images intended to be processedin accordance with the teachings hereof, may be stored in a storagedevice 413 or communicated to a remote device for storage or furtherprocessing over network 414 via a communications interface (not shown).It should be understood that any of the modules and processing units ofFIG. 4 are in communication with workstation 410 via pathways (shown andnot shown) and may further be in communication with one or more devicesover network 414. It should be appreciated that some or all of thefunctionality for any of the modules of system 400 may be performed, inwhole or in part, by components internal to workstation 410 or by aspecial purpose computer system. It should also be appreciated thatvarious modules may designate one or more components which may, in turn,comprise software and/or hardware designed to perform the intendedfunction. A plurality of modules may collectively perform a singlefunction. Each module may have a specialized processor capable ofexecuting machine readable program instructions. A module may comprise asingle piece of hardware such as an ASIC, electronic circuit, or specialpurpose processor. A plurality of modules may be executed by either asingle special purpose computer system or a plurality of special purposecomputer systems operating in parallel. Connections between modulesinclude both physical and logical connections. Modules may furtherinclude one or more software/hardware modules which may further comprisean operating system, drivers, device controllers, and other apparatusessome or all of which may be connected via a network. It is alsocontemplated that one or more aspects of the present method may beimplemented on a dedicated computer system and may also be practiced indistributed computing environments where tasks are performed by remotedevices that are linked through a network.

Other Embodiments

The above-disclosed and other features and functions, or alternativesthereof, may be desirably combined into many other different systems orapplications. Various presently unforeseen or unanticipatedalternatives, modifications, variations, or improvements therein maybecome apparent and/or subsequently made by those skilled in the artwhich are also intended to be encompassed by the following claims.Accordingly, the embodiments set forth above are considered to beillustrative and not limiting. Various changes to the above-describedembodiments may be made without departing from the spirit and scope ofthe invention. The teachings hereof can be implemented in hardware orsoftware using any known or later developed systems, structures,devices, and/or software by those skilled in the applicable art withoutundue experimentation from the functional description provided hereinwith a general knowledge of the relevant arts. Moreover, the methodshereof can be implemented as a routine embedded on a personal computeror as a resource residing on a server or workstation, such as a routineembedded in a plug-in, a driver, or the like. The teachings hereof maybe partially or fully implemented in software using object orobject-oriented software development environments that provide portablesource code that can be used on a variety of computer, workstation,server, network, or other hardware platforms. One or more of thecapabilities hereof can be emulated in a virtual environment as providedby an operating system, specialized programs or leverage off-the-shelfcomputer graphics software such as that in Windows, Java, or from aserver or hardware accelerator. One or more aspects of the methodsdescribed herein are intended to be incorporated in an article ofmanufacture, including one or more computer program products, havingcomputer usable or machine readable media. The article of manufacturemay be included on at least one storage device readable by a machinearchitecture embodying executable program instructions capable ofperforming the methodology described herein. The article of manufacturemay be included as part of a system, an operating system, a plug-in, ormay be shipped, sold, leased, or otherwise provided separately eitheralone or as part of an add-on, update, upgrade, or product suite.

Various of the above-disclosed and other features and functions, oralternatives hereof, may be combined into other systems or applications.Various presently unforeseen or unanticipated alternatives,modifications, variations, or improvements therein may become apparentand/or subsequently made by those skilled in the art which are alsointended to be encompassed by the following claims. Accordingly, theembodiments set forth above are considered to be illustrative and notlimiting. Various changes to the above-described embodiments may be madewithout departing from the spirit and scope of the invention. Theteachings of any printed publications including patents and patentapplications, are each separately hereby incorporated by reference intheir entirety.

What is claimed is:
 1. An infrared (IR) camera for acquiring infraredimages of skin, the IR camera comprising: optics for focusing apercentage of light reflected from an object onto an array of detectorssuch that said reflected light can be spatially resolved to form aninfrared image, said detectors sampling radiation emitted by at leastone light source and recording intensity values for multiple pixelslocations along a two dimensional grid; a first filter with atransmittance peaked in the range of 1000 nm to 1150 nm; a second filterwith a transmittance peaked in the range of 1400 nm to 1500 nm; and athird filter with a transmittance peaked in the range of 1550 nm to 1650nm.
 2. The IR camera of claim 1, wherein said detector array comprisesany combination of: Mercury Cadmium Telluride (HgCdTe) sensors, IndiumArsenide (InAs) sensors, Indium Gallium Arsenide (InGaAs) sensors,Indium Antimonide (InSb) sensors, and Lead Sulphide (PbS) sensors. 3.The IR camera of claim 1, further comprising a fourth filter with atransmittance peaked in the range of 1150 nm to 1400 nm.
 4. The IRcamera of claim 1, wherein said filters comprise a geometricallypatterned array.
 5. The IR camera of claim 1, wherein said filterscomprises any of: a thin film filter for simultaneous multi-imagecapture of different spectral bands with a mosaic pattern betweenfilters, a Fabry-Perot filter for simultaneous multi-image capture ofdifferent spectral bands, and a filter wheel for non-simultaneousmulti-image capture of different spectral bands.
 6. The IR camera ofclaim 1, further comprising a plurality of outputs for outputting atleast one reflectance value per channel.
 7. The IR camera of claim 1,further comprising a processor and a storage device for processing andstoring reflectances associated with pixels in said image.
 8. The IRcamera of claim 1, wherein said detectors are selectable through acontroller.
 9. The IR camera of claim 1, wherein said light sourcecomprises an array of light emitting diodes (LEDs) with each of saiddiodes emitting radiation at one of said wavelength ranges.
 10. The IRcamera of claim 1, wherein said reflected light comprises a time varyingsource video signal.
 11. The IR camera of claim 1, wherein said objectscomprise skin blobs of human skin.
 12. The IR camera of claim 1, whereinsaid images are of human occupants in a motor vehicle.
 13. The IR cameraof claim 1, wherein determining said wavelength band combination of saidfirst, second, and third filters comprises: receiving at least oneinfrared image capturing, in part, an area of exposed skin; splittingthe image data into a training set and a test set; repeating for eachcandidate filter band combination: deriving a classifier for pixels insaid filter bands based on pixel data in said training set; using saidclassifier to classify each pixel in said test set as being skin ornon-skin; and computing an objective function to measure a performanceof said classifier; and selecting a filter band combination whichmaximizes said objective function.
 14. The IR camera of claim 13,wherein said objective function comprises:J=D _(skin) −D _(non-skin) where D_(skin) is a ratio of a number ofcorrectly classified skin pixels to the total number of skin pixels inthe image, and D_(non-skin) is a ratio of a number of non-skin pixelsthat have been incorrectly classified as skin pixels to a total numberof non-skin pixels in said image.
 14. (canceled)
 15. The method of claim16, wherein said objective function comprises:J=D _(skin) −D _(non-skin) where D_(skin) is a ratio of a number ofcorrectly classified skin pixels to the total number of skin pixels inthe image, and D_(non-skin) is a ratio of a number of non-skin pixelsthat have been incorrectly classified as skin pixels to a total numberof non-skin pixels in said image.
 16. A method for determining acombination of bands for filters of an infrared (IR) camera used foracquiring IR images of skin, the method comprising: receiving at leastone infrared image capturing, in part, an area of exposed skin;splitting the image data into a training set and a test set; repeatingfor each candidate filter band combination: deriving a classifier forpixels in said filter bands based on pixel data in said training set;using said classifier to classify each pixel in said test set as beingskin or non-skin; and computing an objective function to measure aperformance of said classifier; and selecting a filter band combinationwhich maximizes said objective function.